{% extends "app7_static_files/flexible_base.html" %} {% load static %} {% block style %} {% endblock %} {% block content %}
You would do it exactly the same way, except you won't be running ./manage.py runserver
anymore.
This means your static files will NOT be discovered automatically : they must be served by a production ready server such as Apache, Nginx, Lighttpd or a CDN.
Since this is not a tutorial, I will not go into details on how you can do each, but only the most common way.
The classic configuration is to have settings.STATIC_ROOT
pointing to a directory where Django should copy all static files.
Then you run ./manage.py collectstatic
.
It will copy them all in there.
Try it. Run the command, then look at the files in settings.STATIC_ROOT
(currently : {{ settings.STATIC_ROOT }}).
There will be a lot of files in there : all files from all 'static' directories from all apps are copied automatically, including the numerous CSS/JS files of the Django 'admin' app.
It will be then your job to create a configuration file for your server so it serves the content of settings.STATIC_ROOT
when a request is made to settings.STATIC_URL
(currently: {{ settings.STATIC_URL }})
I like to use Nginx to serve Django apps. Here is what a minimalist Nginx configuration file could look like:
server { listen 80; # Serving static files. # If the server ask for http://site.com/static/anything, it will # match this and servce 'anything' from the directory containing # all static files. # Django is never called for these files. location ~* ^{{ settings.STATIC_URL }} { root {{ settings.STATIC_ROOT }}; } # Passing other requests to Django. Not the topic of this app :-) location / { proxy_pass http://<IP_ADDRESS>:8000; } }
A similar Apache configuration file would be:
<VirtualHost *:80> # Serving static files. # If the server ask for http://site.com/static/anything, it will # match this and servce 'anything' from the directory containing # all static files. # Apache doesn't like the leading slash on aliases, so don't do # "{{ STRIPPED_STATIC_URL }}/" Alias {{ STRIPPED_STATIC_URL }} {{ settings.STATIC_ROOT }} <Directory {{ settings.STATIC_ROOT }}> Order allow,deny Allow from all </Directory> # Passing other requests to Django. Not the topic of this app :-) WSGIScriptAlias / {{ settings.PROJECT_DIR }}/wsgi.py <Directory {{ settings.PROJECT_DIR }}> Order allow,deny Allow from all </Directory> </VirtualHost>
If you don't have access to Apache configuration but can set rewrite rules in a .httpacess files, you can do something like this:
AddHandler fcgid-script .fcgi RewriteEngine On # Be careful with this, settings.STATIC_URL should match any subpath # of /path/to/apache/root/dir/ since we don't serve it, we REWRITE it. RewriteRule {{ settings.STATIC_URL }} /path/to/apache/root/dir/{{ STRIPPED_STATIC_URL }} RewriteCond %{REQUEST_FILENAME} !-f RewriteRule ^(.*)$ site.fcgi/$1 [QSA,L]
I wrote the paths using template variables, but in the real file, they are hard coded. This is why it's common to generated configuration files using template files.
{% endblock %}


Your browser does not support the HTML5 canvas tag.
Your browser does not support the HTML5 canvas tag.
Chrome
Mona Lisa
My Picture
Start
Stop
Generation 0
Fitness Value: 0%


Every four years, millions of viewers tune in to watch figure skaters and ice hockey stars compete in the Winter Olympics. But what about the sports and athletes that don't participate? Capital News Service's ViewFinder visual storytelling team went behind the scenes to get to know the athletes behind the other games.
Swipe Left
By Bryan Taylor
REISTERSTOWN, Maryland -- A hybrid of ice hockey, soccer and lacrosse, broomball is a fast-paced game with a hard-hitting edge. Beth Williams,
the "chief ice officer" of the Baltimore Broomball Club, talks about the bonds formed over years of playing.
By Pearl Mak
FREDERICK, Maryland -- Aerial yoga instructor Tina Moran turned to aerial yoga as a way to stay active. She discovered the enjoyments of being upside down.
By Lauren Koenig and Mollie Markowitz
FREDERICK, Maryland -- A traditional dance with a modern twist, belly dancing tells the story of body acceptance and female empowerment. Instructor Jessica Welch
explains her personal connection to the dance while showing off her moves.
By Amy Jennings
FREDERICK, Maryland -- The Frederick County Knucklers marbles club spends their Saturday mornings practicing a game that has been around for centuries.
Kids ages 7-14 play marbles at a church in Middletown, Maryland through the spring to prepare for nationals in June.
By Keirsten Owens and Casey Tomchek
WASHINGTON, D.C. -- After moving to Washington, Katie Johnson and Taylor Johnson were looking for a new way to make friends.
They found the DC Bocce League and started a team -- "Ballsagna" -- with six people they now consider their close friends.
By Stephanie Parr
ELKRIDGE, Maryland -- Barbara Borowy has been playing darts with the Howard County Dart Association for over 30 years.
Borowy says too few women play darts, and she hopes more will join in on the fun.
For about five years, the Philip Merrill College of Journalism’s ViewFinder team and Capital News Service have provided deeply reported, award-winning coverage of issues of importance to Marylanders and people around the world.
Amy Jennings, Lauren Koenig, Pearl Mak, Mollie Markowitz, Keirsten Owens, Stephanie Parr, Bryan Taylor and Casey Tomchek.
Sarah Fielder and Uyen Nguyen


As I open
my eyes
roll over
and stretch
my hands have already
without any conscious decision making
reached out to become connected
to the world
I let out a yawn
swipe my thumb
illuminate my mug
and away
I go
less than one minute back into consciousness
I have started my day of digital consumption
I spend my first thirty minutes
hastily skimming
through all my major social medias
did I miss anything
This is followed by an empty-minded flow through Instagram
tapping photos
with a fervor
as if I
was breaking through the fourth wall.
This action finally stops when the realization of time reminds me of my still laying in bed
I get up
switch to listening to a podcast about [whatever]
and tell myself
this is okay
at least I’m learning useful information about what's important to me
I continue this dance until I reach my destination for the majority of the day
After hours of professional content creation
I switch back to the personal
and check back in on my social platforms
This continues into my bed where still
I am rubbing my thumbs around on my phone
staring into the soul of whatever media platform until I decide that sleep is necessary


This is an introduction
This will provide an introduction to filtering for data in BrandsEye
BrandsEye’s API supports a small language to help you select exactly the mentions that you want to select. You’ll do this with the help of the mentions()
function, which always expects a filter. You can also count and aggregate mention data using count_mentions()
. We also have a query language, that this will introduce a bit of.
Combined with dplyr1 for manipulating data, ggplot22 for visualising data, and readr3 for exporting data, brandseyer2 is a powerful tool for examining social media data.
Filtering is selecting individual mentions to make up a data set, either to be returned directly using the mentions()
function, or for counting and aggregating using count_mentions()
.
library(brandseyer2)
account("TEST01AA") %>%
mentions(filter = "published inthelast week")
#> # A tibble: 1 x 7
#> id published brands sentiment mediaLinks socialNetwork tags
#> <chr> <dttm> <list> <int> <list> <chr> <list>
#> 1 2-1 2018-03-03 21:37:35 <int [2… 0 <tibble [1 … TWITTER <int …
All mentions are published at a particular date and time. This is the most important thing that you can filter on: the publication date. Most questions that we want to answer usually have a constrained time period. For example,
what was the sentiment towards our brand last month?
or
how did sentiment change towards our brand over Christmas last year compared to Christmas the year before?
The published
field allows us to filter on mentions by their date of publication. For example,
published after ‘2018/01/01’
will filter for mentions that are published on or after the 1st of January, 2018. Note that mentions published on the first are also included by the filter.
published before ‘2018/02/01’
filters for mentions published strictly before the 1st of February, 2018. It does not include mentions from the 1st of February.
These two filters can be combined using the word and
, like so:
published after ‘2018/01/01’ and published before ‘2018/02/01’
Some things to notice in this filter: it’s made up of two sub-filters, the after bit, and the before bit. These have been combined using an and
. Dates themselves are quoted in single-quotes, like so: ‘2018/01/01’.
Only mentions for which are published both after 2018/01/01 and before 2018/02/02, will be selected by this filter. In other words, this filter selects mentions published during the month of January.
We will have more to say about the connector and
in a later section of this guide.
You can test this out now, using either analyse or brandseyer2.
All of our examples, going forward, will assume that you have access to your accounts, and that you’re using brandseyer2. We won’t do anything fancy, just the bare minimum brandseyer2 to demonstrate the data.
mentions <- account("QUIR01BA") %>%
filter_mentions("published after '2018/01/01' and published before '2018/02/01'" ) %>%
count_mentions
mentions
#> # A tibble: 1 x 1
#> mentionCount
#> <int>
#> 1 36592
So, we have 36 592 mentions published in the month of January. Instead of counting the mentions with count_mentions()
, you could instead use mentions()
to pull a table of all of these mentions.
The filter language provides some convenient shortcuts. A common one is to find mentions from the last week
published inthelast week
Notice that inthelast
is written as one word.
Similarly, we can find mentions
Dates first, since this allows people to constrain data, rather than get everything. How do they cancel if they’re accidently pulling more than they want to?
There are three parts to a filter: a field, an operator, and a value. The field is a part of a mention. The operator looks different depending on whether it’s a number or text.
AND
? Both things must be true.OR
? Either things must be true.Just give me things from twitter
socialNetwork is TWITTER
Social network codes can be obtained using data_model_networks()
Currently, these are
data_model_networks()
#> # A tibble: 9 x 2
#> id name
#> <chr> <chr>
#> 1 TWITTER Twitter
#> 2 FACEBOOK Facebook
#> 3 INSTAGRAM Instagram
#> 4 GOOGLE_PLUS Google Plus
#> 5 LINKEDIN LinkedIn
#> 6 TUMBLR Tumblr
#> 7 VK VK
#> 8 YOUTUBE YouTube
#> 9 TELEGRAM Telegram
Mentions by themselves are not always interesting. Mentions, as handled by BrandsEye, relate towards something that we name a brand. This is not necessarily a brand in the strict sense of the word. It certainly could be, but in this case, it’s the entity or concept or thing that the mention is related towards.
It is also, importantly, the thing that we measure sentiment towards.
Sentiment doesn’t exist in a vacuum, it is usually related to something.
media is ENTERPRISE
We currently only have three main categories at the moment.
| ID | Meaning |
|---|---|
| ENTERPRISE | From the brand itself |
| PRESS | From a media source |
| CONSUMER | From everyone else |
But you can list all the ones we recognise using data_model_categories()
.
process is verified
crowdVerified is true
What’s the difference, and when to use which.
We use two letter SO 3166-1 Alpha 2 country codes to represent the countries themselves. You can list them using data_model_countries()
data_model_countries()
#> # A tibble: 252 x 2
#> id name
#> <chr> <chr>
#> 1 AF Afghanistan
#> 2 AX Aland Islands
#> 3 AL Albania
#> 4 DZ Algeria
#> 5 AS American Samoa
#> 6 AD Andorra
#> 7 AO Angola
#> 8 AI Anguilla
#> 9 AQ Antarctica
#> 10 AG Antigua and Barbuda
#> # ... with 242 more rows
Analyse is required to get all of these names.
Now that we have more statements, we can give more examples.
We use 2-letter ISO 639-1 codes to represent languages. You can list all the ones we understand using data_model_languages()
:
data_model_languages()
#> # A tibble: 149 x 2
#> id name
#> <chr> <chr>
#> 1 ab Abkhazian
#> 2 aa Afar
#> 3 af Afrikaans
#> 4 sq Albanian
#> 5 am Amharic
#> 6 ar Arabic
#> 7 an Aragonese
#> 8 hy Armenian
#> 9 as Assamese
#> 10 ay Aymara
#> # ... with 139 more rows
dplyr is an amazing library for manipulating data. After downloading data using brandseyer2, it allows you to reshape the data to fit your needs.↩
ggplot2 is a rich visualisation library for R. After pulling data from brandseyer2, ggplot2 is a great tool for visualising the data.↩
readr is a library for reading and writing CSV data. R has a rich ecosystem for importing and exporting data, with other libraries providing support for Excel and Google Sheets.↩


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud | |
|---|---|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| VCloudError
|
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.binders | |
|---|---|
| Catalog
|
|
| CatalogItem
|
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VDC
A vDC is a deployment environment for vApps. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.compute.config | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Status
Objects such as vAppTemplate, vApp, and Vm have a status attribute whose value indicates the state of the object. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.compute.functions | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Status
Objects such as vAppTemplate, vApp, and Vm have a status attribute whose value indicates the state of the object. |
|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.compute.strategy | |
|---|---|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.compute.util | |
|---|---|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.config | |
|---|---|
| Catalog
|
|
| CatalogItem
|
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VCloudSession
|
|
| VDC
A vDC is a deployment environment for vApps. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.domain | |
|---|---|
| AllocationModel
The AllocationModel element defines how resources are allocated in a vDC. |
|
| Capacity
reports storage resource consumption in a vDC. |
|
| GuestCustomizationSection
The GuestCustomization of a Vm contains customization parameters for the guest operating system of the virtual machine. |
|
| NetworkConnection
describes a single network connection. |
|
| NetworkConnection.Builder
|
|
| NetworkConnectionSection
The NetworkConnectionSection element specifies how a Vm is connected to a vApp network. |
|
| NetworkConnectionSection.Builder
|
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Status
Objects such as vAppTemplate, vApp, and Vm have a status attribute whose value indicates the state of the object. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| TaskStatus
|
|
| VCloudError
|
|
| VCloudError.MinorCode
|
|
| VDCStatus
The creation status of the vDC |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.domain.internal | |
|---|---|
| AllocationModel
The AllocationModel element defines how resources are allocated in a vDC. |
|
| Capacity
reports storage resource consumption in a vDC. |
|
| Catalog
|
|
| CatalogItem
|
|
| GuestCustomizationSection
The GuestCustomization of a Vm contains customization parameters for the guest operating system of the virtual machine. |
|
| NetworkConnectionSection
The NetworkConnectionSection element specifies how a Vm is connected to a vApp network. |
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Status
Objects such as vAppTemplate, vApp, and Vm have a status attribute whose value indicates the state of the object. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| TasksList
|
|
| TaskStatus
|
|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VCloudError
|
|
| VCloudError.MinorCode
|
|
| VDC
A vDC is a deployment environment for vApps. |
|
| VDCStatus
The creation status of the vDC |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.domain.network | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.domain.network.internal | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.domain.ovf | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.features | |
|---|---|
| Catalog
|
|
| CatalogItem
|
|
| GuestCustomizationSection
The GuestCustomization of a Vm contains customization parameters for the guest operating system of the virtual machine. |
|
| NetworkConnectionSection
The NetworkConnectionSection element specifies how a Vm is connected to a vApp network. |
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| TasksList
|
|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VDC
A vDC is a deployment environment for vApps. |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.functions | |
|---|---|
| Catalog
|
|
| CatalogItem
|
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VCloudSession
|
|
| VDC
A vDC is a deployment environment for vApps. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.internal | |
|---|---|
| VCloudSession
|
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.loaders | |
|---|---|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.suppliers | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.util | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| VCloudError
|
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.xml | |
|---|---|
| AllocationModel
The AllocationModel element defines how resources are allocated in a vDC. |
|
| Capacity
reports storage resource consumption in a vDC. |
|
| Catalog
|
|
| CatalogItem
|
|
| GuestCustomizationSection
The GuestCustomization of a Vm contains customization parameters for the guest operating system of the virtual machine. |
|
| NetworkConnection
describes a single network connection. |
|
| NetworkConnectionSection
The NetworkConnectionSection element specifies how a Vm is connected to a vApp network. |
|
| Org
A vCloud organization is a high-level abstraction that provides a unit of administration for objects and resources. |
|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
| Status
Objects such as vAppTemplate, vApp, and Vm have a status attribute whose value indicates the state of the object. |
|
| Task
Whenever the result of a request cannot be returned immediately, the server creates a Task object. |
|
| TasksList
|
|
| VApp
A VApp is the result of instantiation of a VAppTemplate . |
|
| VAppTemplate
A VAppTemplate is an abstract description of a vApp. |
|
| VCloudError
|
|
| VDC
A vDC is a deployment environment for vApps. |
|
| VDCStatus
The creation status of the vDC |
|
| Vm
A Vm represents a virtual machine, a member of a vApp’s Children container. |
| Classes in org.jclouds.vcloud.domain used by org.jclouds.vcloud.xml.ovf | |
|---|---|
| ReferenceType
Many container elements are populated with references to contained objects. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


QVideoWidget Class
The QVideoWidget class provides a widget which presents video produced by a media object. More...
| Header: | #include <QVideoWidget> |
| qmake: | QT += multimediawidgets |
| Inherits: | QWidget and QMediaBindableInterface |
| Inherited By: |
Properties
|
|
- 59 properties inherited from QWidget
Public Functions
| QVideoWidget(QWidget *parent = Q_NULLPTR) | |
| ~QVideoWidget() | |
| Qt::AspectRatioMode | aspectRatioMode() const |
| int | brightness() const |
| int | contrast() const |
| int | hue() const |
| bool | isFullScreen() const |
| int | saturation() const |
Reimplemented Public Functions
| virtual QMediaObject * | mediaObject() const override |
| virtual QSize | sizeHint() const override |
- 216 public functions inherited from QWidget
- 1 public function inherited from QMediaBindableInterface
Public Slots
| void | setAspectRatioMode(Qt::AspectRatioMode mode) |
| void | setBrightness(int brightness) |
| void | setContrast(int contrast) |
| void | setFullScreen(bool fullScreen) |
| void | setHue(int hue) |
| void | setSaturation(int saturation) |
- 19 public slots inherited from QWidget
Signals
| void | brightnessChanged(int brightness) |
| void | contrastChanged(int contrast) |
| void | fullScreenChanged(bool fullScreen) |
| void | hueChanged(int hue) |
| void | saturationChanged(int saturation) |
- 3 signals inherited from QWidget
Protected Functions
| bool | event(QEvent *event) override |
Reimplemented Protected Functions
| virtual void | hideEvent(QHideEvent *event) override |
| virtual void | moveEvent(QMoveEvent *event) override |
| virtual void | paintEvent(QPaintEvent *event) override |
| virtual void | resizeEvent(QResizeEvent *event) override |
| virtual void | showEvent(QShowEvent *event) override |
- 35 protected functions inherited from QWidget
- 1 protected function inherited from QMediaBindableInterface
Additional Inherited Members
Detailed Description
The QVideoWidget class provides a widget which presents video produced by a media object.
Attaching a QVideoWidget to a QMediaObject allows it to display the video or image output of that media object. A QVideoWidget is attached to media object by passing a pointer to the QMediaObject in its constructor, and detached by destroying the QVideoWidget.
player = new QMediaPlayer; playlist = new QMediaPlaylist(player); playlist->addMedia(QUrl("http://example.com/myclip1.mp4")); playlist->addMedia(QUrl("http://example.com/myclip2.mp4")); videoWidget = new QVideoWidget; player->setVideoOutput(videoWidget); videoWidget->show(); playlist->setCurrentIndex(1); player->play();
Note: Only a single display output can be attached to a media object at one time.
See also QMediaObject, QMediaPlayer, and QGraphicsVideoItem.
Property Documentation
aspectRatioMode : Qt::AspectRatioMode
how video is scaled with respect to its aspect ratio.
Access functions:
| Qt::AspectRatioMode | aspectRatioMode() const |
| void | setAspectRatioMode(Qt::AspectRatioMode mode) |
brightness : int
This property holds an adjustment to the brightness of displayed video.
Valid brightness values range between -100 and 100, the default is 0.
Access functions:
| int | brightness() const |
| void | setBrightness(int brightness) |
Notifier signal:
| void | brightnessChanged(int brightness) |
contrast : int
This property holds an adjustment to the contrast of displayed video.
Valid contrast values range between -100 and 100, the default is 0.
Access functions:
| int | contrast() const |
| void | setContrast(int contrast) |
Notifier signal:
| void | contrastChanged(int contrast) |
fullScreen : bool
This property holds whether video display is confined to a window or is fullScreen.
Access functions:
| bool | isFullScreen() const |
| void | setFullScreen(bool fullScreen) |
Notifier signal:
| void | fullScreenChanged(bool fullScreen) |
hue : int
This property holds an adjustment to the hue of displayed video.
Valid hue values range between -100 and 100, the default is 0.
Access functions:
| int | hue() const |
| void | setHue(int hue) |
Notifier signal:
| void | hueChanged(int hue) |
mediaObject : QMediaObject * const
This property holds the media object which provides the video displayed by a widget.
Access functions:
| virtual QMediaObject * | mediaObject() const override |
saturation : int
This property holds an adjustment to the saturation of displayed video.
Valid saturation values range between -100 and 100, the default is 0.
Access functions:
| int | saturation() const |
| void | setSaturation(int saturation) |
Notifier signal:
| void | saturationChanged(int saturation) |
Member Function Documentation
QVideoWidget::QVideoWidget(QWidget *parent = Q_NULLPTR)
Constructs a new video widget.
The parent is passed to QWidget.
QVideoWidget::~QVideoWidget()
Destroys a video widget.
[signal]
void QVideoWidget::brightnessChanged(int brightness)
Signals that a video widgets's brightness adjustment has changed.
Note: Notifier signal for property brightness.
See also brightness().
[signal]
void QVideoWidget::contrastChanged(int contrast)
Signals that a video widgets's contrast adjustment has changed.
Note: Notifier signal for property contrast.
See also contrast().
[protected]
bool QVideoWidget::event(QEvent *event)
Current event event. Returns the value of the baseclass QWidget::event(QEvent *event) function.
[signal]
void QVideoWidget::fullScreenChanged(bool fullScreen)
Signals that the fullScreen mode of a video widget has changed.
Note: Notifier signal for property fullScreen.
See also isFullScreen().
[override virtual protected]
void QVideoWidget::hideEvent(QHideEvent *event)
Reimplemented from QWidget::hideEvent().
Handles the hide event.
[signal]
void QVideoWidget::hueChanged(int hue)
Signals that a video widgets's hue has changed.
Note: Notifier signal for property hue.
See also hue().
[override virtual protected]
void QVideoWidget::moveEvent(QMoveEvent *event)
Reimplemented from QWidget::moveEvent().
Handles the move event.
[override virtual protected]
void QVideoWidget::paintEvent(QPaintEvent *event)
Reimplemented from QWidget::paintEvent().
Handles the paint event.
[override virtual protected]
void QVideoWidget::resizeEvent(QResizeEvent *event)
Reimplemented from QWidget::resizeEvent().
Handles the resize event.
[signal]
void QVideoWidget::saturationChanged(int saturation)
Signals that a video widgets's saturation has changed.
Note: Notifier signal for property saturation.
See also saturation().
[override virtual protected]
void QVideoWidget::showEvent(QShowEvent *event)
Reimplemented from QWidget::showEvent().
Handles the show event.
[override virtual]
QSize QVideoWidget::sizeHint() const
Reimplemented from QWidget::sizeHint().
Returns the size hint for the current back end, if there is one, or else the size hint from QWidget.


Happy Customers
Leno is one of the easiest and feature packed marketing automation apps in the market. Download it today!
For Apple For AndroidLike any self improving process, everything starts with setting your goals and committing to them
Leno provides a well designed and ergonomic visual editor for you to edit your quick notes
Each option packaged in the app's menus is provided in order to improve you personally
Schedule your appointments, meetings and periodical evaluations using the tools
Reading focus mode for long form articles, ebooks and other materials with long text
Get a solid foundation for your self development efforts. Try Leno mobile app for devices
After you've configured the app and settled on the data gathering techniques you can start the information trackers and begin collecting those long awaited interesting details.
Get a solid foundation for your self development efforts. Try Leno mobile app now
Reading focus mode for long form articles, ebooks and other materials with long text
Like any self improving process, everything starts with setting goals and comiting
Schedule your appointments, meetings and periodical evaluations using the tools
Leno provides a well designed and ergonomic visual editor for you to edit your notes
Each option packaged in the app's menus is provided in order to improve you personally
Get a solid foundation for your self development efforts. Try Leno mobile app today
Reading focus mode for long form articles, ebooks and other materials with long text
Like any self improving process, everything starts with setting your goals and comiting
Schedule your appointments, meetings and periodical evaluations using the tools
Leno provides a well designed and ergonomic visual editor for you to edit your notes
Each option packaged in the app's menus is provided in order to improve you personally
Monitor the evolution of your finances and health state using tools integrated in Leno. The generated real time reports can be filtered based on any desired criteria.
Leno can easily help you track your personal development evolution if you take the time to properly setup your goals at the beginning of the training process. Check it out
LightboxThe app can easily help you track your personal development evolution if you take the time to set it up.
This is a great app which can help you save time and make your live easier. And it will help improve your productivity.
The calendar input function enables the user to setup training, meditation and relaxation sessions with ease. Just open the feature and start setting your time
LightboxHappy Customers
Good Reviews
Press Articles
Solved Issues
Future Updates
Copyright © Template by Inovatik


Paul Heckbert
Pixar
ph 40 < ph.c > foo.c; cc foo.c -o ph
ph 20 < a_C_prog.c > bar.c; cc bar.c
The judges used this program to fold up very long lines which the BSD fold(1) was unable to process. This program has been added to the contest tool chest.
On System V systems, compile with: -Dindex=strchr
To compile on a 16 bit machine, change 300000’s to 30000.
Copyright © 1987, Landon Curt Noll & Larry Bassel. All Rights Reserved. Permission for personal, educational or non-profit use is granted provided this this copyright and notice are included in its entirety and remains unaltered. All other uses must receive prior permission in writing from both Landon Curt Noll and Larry Bassel.


Links to our pages for Week 9 of BT3103 lesson:
The great floorball group
Alena
Brenda
Nikolas
Wen Qing


Mailing List: This Google Group serves as our mailing list. Send an email to<EMAIL_ADDRESS>to participate.
Orem Meetup: We meet in Orem the first Thursday of every month. See topics and RSVP on our Meetup.com page.
SLC Meetup: We meet in Salt Lake County the third Tuesday of every month. See topics and RSVP on our Meetup.com page.
Videos: When we can, we record our speakers. See our YouTube channel.
LinkedIn: Join our LinkedIn group to network with us.
Slack: Request an invite at slack.utahjavascript.com.
Conferences: We hold a conference every year. See conf.utahjs.com for details.
More info coming soon!


Revenue may be increased either by increases in price or volume for a given product.
Insight into business issues may be derived by breaking down volume by product line, country, distribution channel or retail location.


Artificial neural networks are computational systems that can learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. It is supposed to mimic a biological system, wherein neurons interact by sending signals in the form of mathematical functions between layers. All layers can contain an arbitrary number of neurons, and each connection is represented by a weight variable.
Each time we describe a neural network algorithm we will typically specify three things.
The field of artificial neural networks has a long history of development, and is closely connected with the advancement of computer science and computers in general. A model of artificial neurons was first developed by McCulloch and Pitts in 1943 to study signal processing in the brain and has later been refined by others. The general idea is to mimic neural networks in the human brain, which is composed of billions of neurons that communicate with each other by sending electrical signals. Each neuron accumulates its incoming signals, which must exceed an activation threshold to yield an output. If the threshold is not overcome, the neuron remains inactive, i.e. has zero output.
This behaviour has inspired a simple mathematical model for an artificial neuron. $$ \begin{equation} y = f\left(\sum_{i=1}^n w_jx_j + b \right) = f(z), \label{artificialNeuron} \end{equation} $$ where the bias \( b \) is sometimes denoted \( w_0 \). Here, the output \( y \) of the neuron is the value of its activation function, which have as input a weighted sum of signals \( x_1, \dots ,x_n \) received by \( n \) other neurons.
Conceptually, it is helpful to divide neural networks into four categories:
In quantum information theory, it has been shown that one can perform gate decompositions with the help of neural networks.
The applications are not limited to the natural sciences. There is a plethora of applications in essentially all disciplines, from the humanities to life science and medicine.
An artificial neural network (ANN), is a computational model that consists of layers of connected neurons, or nodes or units. We will refer to these interchangeably as units or nodes, and sometimes as neurons.
It is supposed to mimic a biological nervous system by letting each neuron interact with other neurons by sending signals in the form of mathematical functions between layers. A wide variety of different ANNs have been developed, but most of them consist of an input layer, an output layer and eventual layers in-between, called hidden layers. All layers can contain an arbitrary number of nodes, and each connection between two nodes is associated with a weight variable.
Neural networks (also called neural nets) are neural-inspired nonlinear models for supervised learning. As we will see, neural nets can be viewed as natural, more powerful extensions of supervised learning methods such as linear and logistic regression and soft-max methods we discussed earlier.
The feed-forward neural network (FFNN) was the first and simplest type of ANNs that were devised. In this network, the information moves in only one direction: forward through the layers.
Nodes are represented by circles, while the arrows display the connections between the nodes, including the direction of information flow. Additionally, each arrow corresponds to a weight variable (figure to come). We observe that each node in a layer is connected to all nodes in the subsequent layer, making this a so-called fully-connected FFNN.
A different variant of FFNNs are convolutional neural networks (CNNs), which have a connectivity pattern inspired by the animal visual cortex. Individual neurons in the visual cortex only respond to stimuli from small sub-regions of the visual field, called a receptive field. This makes the neurons well-suited to exploit the strong spatially local correlation present in natural images. The response of each neuron can be approximated mathematically as a convolution operation. (figure to come)
Convolutional neural networks emulate the behaviour of neurons in the visual cortex by enforcing a local connectivity pattern between nodes of adjacent layers: Each node in a convolutional layer is connected only to a subset of the nodes in the previous layer, in contrast to the fully-connected FFNN. Often, CNNs consist of several convolutional layers that learn local features of the input, with a fully-connected layer at the end, which gathers all the local data and produces the outputs. They have wide applications in image and video recognition.
So far we have only mentioned ANNs where information flows in one direction: forward. Recurrent neural networks on the other hand, have connections between nodes that form directed cycles. This creates a form of internal memory which are able to capture information on what has been calculated before; the output is dependent on the previous computations. Recurrent NNs make use of sequential information by performing the same task for every element in a sequence, where each element depends on previous elements. An example of such information is sentences, making recurrent NNs especially well-suited for handwriting and speech recognition.
There are many other kinds of ANNs that have been developed. One type that is specifically designed for interpolation in multidimensional space is the radial basis function (RBF) network. RBFs are typically made up of three layers: an input layer, a hidden layer with non-linear radial symmetric activation functions and a linear output layer (''linear'' here means that each node in the output layer has a linear activation function). The layers are normally fully-connected and there are no cycles, thus RBFs can be viewed as a type of fully-connected FFNN. They are however usually treated as a separate type of NN due the unusual activation functions.
The multilayer perceptron (MLP) is a very popular, and easy to implement approach, to deep learning. It consists of
The number of input nodes does not need to equal the number of output nodes. This applies also to the hidden layers. Each layer may have its own number of nodes and activation functions.
The hidden layers have their name from the fact that they are not linked to observables and as we will see below when we define the so-called activation \( \boldsymbol{z} \), we can think of this as a basis expansion of the original inputs \( \boldsymbol{x} \).
According to the universal approximation theorem, a feed-forward neural network with just a single hidden layer containing a finite number of neurons can approximate a continuous multidimensional function to arbitrary accuracy, assuming the activation function for the hidden layer is a non-constant, bounded and monotonically-increasing continuous function. The theorem thus states that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters. It is the multilayer feedforward architecture itself which gives neural networks the potential of being universal approximators.
Note that the requirements on the activation function only applies to the hidden layer, the output nodes are always assumed to be linear, so as to not restrict the range of output values.
The output \( y \) is produced via the activation function \( f \) $$ y = f\left(\sum_{i=1}^n w_ix_i + b \right) = f(z), $$ This function receives \( x_i \) as inputs. Here the activation \( z=(\sum_{i=1}^n w_ix_i+b) \). In an FFNN of such neurons, the inputs \( x_i \) are the outputs of the neurons in the preceding layer. Furthermore, an MLP is fully-connected, which means that each neuron receives a weighted sum of the outputs of all neurons in the previous layer.
First, for each node \( j \) in the first hidden layer, we calculate a weighted sum \( z_j^1 \) of the input coordinates \( x_i \), $$ \begin{equation} z_j^1 = \sum_{i=1}^{n} w_{ji}^1 x_i + b_j^1 \label{_auto1} \end{equation} $$
Here \( b_j^1 \) is the so-called bias which is normally needed in case of zero activation weights or inputs. How to fix the biases and the weights will be discussed below. The value of \( z_j^1 \) is the argument to the activation function \( f \) of each node \( j \), The variable \( n \) stands for all possible inputs to a given node \( j \) in the first layer. We define the output \( y_j^1 \) of all neurons in layer 1 as $$ \begin{equation} y_j^1 = f(z_j^1) = f\left(\sum_{i=1}^n w_{ji}^1 x_i + b_j^1\right), \label{outputLayer1} \end{equation} $$ where we assume that all nodes in the same layer have identical activation functions, hence the notation \( f \). In general, we could assume in the more general case that different layers have different activation functions. In this case we would identify these functions with a superscript \( l \) for the \( l \)-th layer, $$ \begin{equation} y_i^l = f^l(z_i^l) = f^l\left(\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\right), \label{generalLayer} \end{equation} $$ where \( N_{l-1} \) is the number of nodes in layer \( l-1 \). When the output of all the nodes in the first hidden layer are computed, the values of the subsequent layer can be calculated and so forth until the output is obtained.
The output of neuron \( i \) in layer 2 is thus, $$ \begin{align} y_i^2 &= f^2\left(\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\right) \label{_auto2}\\ &= f^2\left[\sum_{j=1}^N w_{ij}^2f^1\left(\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\right) + b_i^2\right] \label{outputLayer2} \end{align} $$ where we have substituted \( y_k^1 \) with the inputs \( x_k \). Finally, the ANN output reads $$ \begin{align} y_i^3 &= f^3\left(\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\right) \label{_auto3}\\ &= f^3\left[\sum_{j} w_{ij}^3 f^2\left(\sum_{k} w_{jk}^2 f^1\left(\sum_{m} w_{km}^1 x_m + b_k^1\right) + b_j^2\right) + b_1^3\right] \label{_auto4} \end{align} $$
We can generalize this expression to an MLP with \( L \) hidden layers. The complete functional form is, $$ \begin{align} &y^{L+1}_i = f^{L+1}\left[\!\sum_{j=1}^{N_L} w_{ij}^L f^L \left(\sum_{k=1}^{N_{L-1}}w_{jk}^{L-1}\left(\dots f^1\left(\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\right)\dots\right)+b_k^{L-1}\right)+b_1^L\right] && \label{completeNN} \end{align} $$ which illustrates a basic property of MLPs: The only independent variables are the input values \( x_n \).
This confirms that an MLP, despite its quite convoluted mathematical form, is nothing more than an analytic function, specifically a mapping of real-valued vectors \( \boldsymbol{x} \in \mathbb{R}^n \rightarrow \boldsymbol{y} \in \mathbb{R}^m \).
Furthermore, the flexibility and universality of an MLP can be illustrated by realizing that the expression is essentially a nested sum of scaled activation functions of the form $$ \begin{equation} f(x) = c_1 f(c_2 x + c_3) + c_4, \label{_auto5} \end{equation} $$ where the parameters \( c_i \) are weights and biases. By adjusting these parameters, the activation functions can be shifted up and down or left and right, change slope or be rescaled which is the key to the flexibility of a neural network.
We can introduce a more convenient notation for the activations in an ANN.
Additionally, we can represent the biases and activations as layer-wise column vectors \( \boldsymbol{b}_l \) and \( \boldsymbol{y}_l \), so that the \( i \)-th element of each vector is the bias \( b_i^l \) and activation \( y_i^l \) of node \( i \) in layer \( l \) respectively.
We have that \( \boldsymbol{W}_l \) is an \( N_{l-1} \times N_l \) matrix, while \( \boldsymbol{b}_l \) and \( \boldsymbol{y}_l \) are \( N_l \times 1 \) column vectors. With this notation, the sum becomes a matrix-vector multiplication, and we can write the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as $$ \begin{equation} \boldsymbol{y}_2 = f_2(\boldsymbol{W}_2 \boldsymbol{y}_{1} + \boldsymbol{b}_{2}) = f_2\left(\left[\begin{array}{ccc} w^2_{11} &w^2_{12} &w^2_{13} \\ w^2_{21} &w^2_{22} &w^2_{23} \\ w^2_{31} &w^2_{32} &w^2_{33} \\ \end{array} \right] \cdot \left[\begin{array}{c} y^1_1 \\ y^1_2 \\ y^1_3 \\ \end{array}\right] + \left[\begin{array}{c} b^2_1 \\ b^2_2 \\ b^2_3 \\ \end{array}\right]\right). \label{_auto6} \end{equation} $$
The activation of node \( i \) in layer 2 is $$ \begin{equation} y^2_i = f_2\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\Bigr) = f_2\left(\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\right). \label{_auto7} \end{equation} $$
This is not just a convenient and compact notation, but also a useful and intuitive way to think about MLPs: The output is calculated by a series of matrix-vector multiplications and vector additions that are used as input to the activation functions. For each operation \( \mathrm{W}_l \boldsymbol{y}_{l-1} \) we move forward one layer.
A property that characterizes a neural network, other than its connectivity, is the choice of activation function(s). The following restrictions are imposed on an activation function for a FFNN to fulfill the universal approximation theorem
Regardless of the number of layers, the output of the NN will be nothing but a linear function of the inputs. Thus we need to introduce some kind of non-linearity to the NN to be able to fit non-linear functions Typical examples are the logistic Sigmoid $$ f_\mathrm{sigmoid}(z) = \frac{1}{1 + e^{-z}}, $$ and the hyperbolic tangent function $$ f_\mathrm{tanh}(z) = \tanh(z) $$
Rectifier activation functions The Rectifier Linear Unit (ReLU) uses the following activation function $$ f_\mathrm{ReLU}(z) = \max(0,z). $$
To solve a problem of dying ReLU neurons, practitioners often use a variant of the ReLU function, such as the leaky ReLU or the so-called exponential linear unit (ELU) function $$ f_\mathrm{ELU}(z) = \left\{\begin{array}{cc} \alpha\left( \exp{(z)}-1\right) & z < 0,\\ z & z \ge 0.\end{array}\right. $$
The sigmoid function are more biologically plausible because the output of inactive neurons are zero. Such activation function are called one-sided. However, it has been shown that the hyperbolic tangent performs better than the sigmoid for training MLPs. It has become the most popular for deep neural networks
Note: figures will be inserted later!
As we have seen the final output of a feed-forward network can be expressed in terms of basic matrix-vector multiplications. The unknowwn quantities are our weights \( w_{ij} \) and we need to find an algorithm for changing them so that our errors are as small as possible. This leads us to the famous back propagation algorithm.
The questions we want to ask are how do changes in the biases and the weights in our network change the cost function and how can we use the final output to modify the weights?
To derive these equations let us start with a plain regression problem and define our cost function as $$ {\cal C}(\boldsymbol{W}) = \frac{1}{2}\sum_{i=1}^n\left(y_i - t_i\right)^2, $$
where the $t_i$s are our \( n \) targets (the values we want to reproduce), while the outputs of the network after having propagated all inputs \( \boldsymbol{x} \) are given by \( y_i \). Other cost functions can also be considered.
With our definition of the targets \( \boldsymbol{t} \), the outputs of the network \( \boldsymbol{y} \) and the inputs \( \boldsymbol{x} \) we define now the activation \( z_j^l \) of node/neuron/unit \( j \) of the \( l \)-th layer as a function of the bias, the weights which add up from the previous layer \( l-1 \) and the forward passes/outputs \( \boldsymbol{a}^{l-1} \) from the previous layer as $$ z_j^l = \sum_{i=1}^{M_{l-1}}w_{ij}^la_i^{l-1}+b_j^l, $$
where \( b_j^l \) are the biases from layer \( l \). Here \( M_{l-1} \) represents the total number of nodes/neurons/units of layer \( l-1 \). The figure here illustrates this equation. We can rewrite this in a more compact form as the matrix-vector products we discussed earlier, $$ \boldsymbol{z}^l = \left(\boldsymbol{W}^l\right)^T\boldsymbol{a}^{l-1}+\boldsymbol{b}^l. $$
With the activation values \( \boldsymbol{z}^l \) we can in turn define the output of layer \( l \) as \( \boldsymbol{a}^l = f(\boldsymbol{z}^l) \) where \( f \) is our activation function. In the examples here we will use the sigmoid function discussed in the logistic regression lecture. We will also use the same activation function \( f \) for all layers and their nodes. It means we have $$ a_j^l = f(z_j^l) = \frac{1}{1+\exp{-(z_j^l)}}. $$
From the definition of the activation \( z_j^l \) we have $$ \frac{\partial z_j^l}{\partial w_{ij}^l} = a_i^{l-1}, $$ and $$ \frac{\partial z_j^l}{\partial a_i^{l-1}} = w_{ji}^l. $$
With our definition of the activation function we have (note that this function depends only on \( z_j^l \)) $$ \frac{\partial a_j^l}{\partial z_j^{l}} = a_j^l(1-a_j^l)=f(z_j^l) \left[ 1-f(z_j^l) \right]. $$
With these definitions we can now compute the derivative of the cost function in terms of the weights.
Let us specialize to the output layer \( l=L \). Our cost function is $$ {\cal C}(\boldsymbol{W^L}) = \frac{1}{2}\sum_{i=1}^n\left(y_i - t_i\right)^2=\frac{1}{2}\sum_{i=1}^n\left(a_i^L - t_i\right)^2, $$ The derivative of this function with respect to the weights is $$ \frac{\partial{\cal C}(\boldsymbol{W^L})}{\partial w_{jk}^L} = \left(a_j^L - t_j\right)\frac{\partial a_j^L}{\partial w_{jk}^{L}}, $$ The last partial derivative can easily be computed and reads (by applying the chain rule) $$ \frac{\partial a_j^L}{\partial w_{jk}^{L}} = \frac{\partial a_j^L}{\partial z_{j}^{L}}\frac{\partial z_j^L}{\partial w_{jk}^{L}}=a_j^L(1-a_j^L)a_k^{L-1}, $$
We have thus $$ \frac{\partial{\cal C}(\boldsymbol{W^L})}{\partial w_{jk}^L} = \left(a_j^L - t_j\right)a_j^L(1-a_j^L)a_k^{L-1}, $$
Defining $$ \delta_j^L = a_j^L(1-a_j^L)\left(a_j^L - t_j\right) = f'(z_j^L)\frac{\partial {\cal C}}{\partial (a_j^L)}, $$ and using the Hadamard product of two vectors we can write this as $$ \boldsymbol{\delta}^L = f'(\boldsymbol{z}^L)\circ\frac{\partial {\cal C}}{\partial (\boldsymbol{a}^L)}. $$
This is an important expression. The second term on the right handside measures how fast the cost function is changing as a function of the $j$th output activation. If, for example, the cost function doesn't depend much on a particular output node \( j \), then \( \delta_j^L \) will be small, which is what we would expect. The first term on the right, measures how fast the activation function \( f \) is changing at a given activation value \( z_j^L \).
Notice that everything in the above equations is easily computed. In particular, we compute \( z_j^L \) while computing the behaviour of the network, and it is only a small additional overhead to compute \( f'(z^L_j) \). The exact form of the derivative with respect to the output depends on the form of the cost function. However, provided the cost function is known there should be little trouble in calculating $$ \frac{\partial {\cal C}}{\partial (a_j^L)} $$
With the definition of \( \delta_j^L \) we have a more compact definition of the derivative of the cost function in terms of the weights, namely $$ \frac{\partial{\cal C}(\boldsymbol{W^L})}{\partial w_{jk}^L} = \delta_j^La_k^{L-1}. $$
It is also easy to see that our previous equation can be written as $$ \delta_j^L =\frac{\partial {\cal C}}{\partial z_j^L}= \frac{\partial {\cal C}}{\partial a_j^L}\frac{\partial a_j^L}{\partial z_j^L}, $$ which can also be interpreted as the partial derivative of the cost function with respect to the biases \( b_j^L \), namely $$ \delta_j^L = \frac{\partial {\cal C}}{\partial b_j^L}\frac{\partial b_j^L}{\partial z_j^L}=\frac{\partial {\cal C}}{\partial b_j^L}, $$ That is, the error \( \delta_j^L \) is exactly equal to the rate of change of the cost function as a function of the bias.
We have now three equations that are essential for the computations of the derivatives of the cost function at the output layer. These equations are needed to start the algorithm and they are
$$ \begin{equation} \frac{\partial{\cal C}(\boldsymbol{W^L})}{\partial w_{jk}^L} = \delta_j^La_k^{L-1}, \label{_auto8} \end{equation} $$ and $$ \begin{equation} \delta_j^L = f'(z_j^L)\frac{\partial {\cal C}}{\partial (a_j^L)}, \label{_auto9} \end{equation} $$ and $$ \begin{equation} \delta_j^L = \frac{\partial {\cal C}}{\partial b_j^L}, \label{_auto10} \end{equation} $$
An interesting consequence of the above equations is that when the activation \( a_k^{L-1} \) is small, the gradient term, that is the derivative of the cost function with respect to the weights, will also tend to be small. We say then that the weight learns slowly, meaning that it changes slowly when we minimize the weights via say gradient descent. In this case we say the system learns slowly.
Another interesting feature is that is when the activation function, represented by the sigmoid function here, is rather flat when we move towards its end values \( 0 \) and \( 1 \). In these cases, the derivatives of the activation function will also be close to zero, meaning again that the gradients will be small and the network learns slowly again.
We need a fourth equation and we are set. We are going to propagate backwards in order to determine the weights and biases. In order to do so we need to represent the error in the layer before the final one \( L-1 \) in terms of the errors in the final output layer.
We have that (replacing \( L \) with a general layer \( l \)) $$ \delta_j^l =\frac{\partial {\cal C}}{\partial z_j^l}. $$ We want to express this in terms of the equations for layer \( l+1 \). Using the chain rule and summing over all \( k \) entries we have $$ \delta_j^l =\sum_k \frac{\partial {\cal C}}{\partial z_k^{l+1}}\frac{\partial z_k^{l+1}}{\partial z_j^{l}}=\sum_k \delta_k^{l+1}\frac{\partial z_k^{l+1}}{\partial z_j^{l}}, $$ and recalling that $$ z_j^{l+1} = \sum_{i=1}^{M_{l}}w_{ij}^{l+1}a_i^{l}+b_j^{l+1}, $$ with \( M_l \) being the number of nodes in layer \( l \), we obtain $$ \delta_j^l =\sum_k \delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l), $$ This is our final equation.
We are now ready to set up the algorithm for back propagation and learning the weights and biases.
The four equations provide us with a way of computing the gradient of the cost function. Let us write this out in the form of an algorithm.
The parameter \( \eta \) is the learning rate. Here it is convenient to use stochastic gradient descent with mini-batches and an outer loop that steps through multiple epochs of training.
The back-propagation algorithm works by going from the output layer to the input layer, propagating the error gradient. The learning algorithm uses these gradients to update each parameter with a Gradient Descent (GD) step.
Unfortunately, the gradients often get smaller and smaller as the algorithm progresses down to the first hidden layers. As a result, the GD update step leaves the lower layer connection weights virtually unchanged, and training never converges to a good solution. This is known in the literature as the vanishing gradients problem.
In other cases, the opposite can happen, namely that the gradients grow bigger and bigger. The result is that many of the layers get large updates of the weights and the learning algorithm diverges. This is the exploding gradients problem, which is mostly encountered in recurrent neural networks. More generally, deep neural networks suffer from unstable gradients, different layers may learn at widely different speeds
Although this unfortunate behavior has been empirically observed for quite a while (it was one of the reasons why deep neural networks were mostly abandoned for a long time), it is only around 2010 that significant progress was made in understanding it.
A paper titled Understanding the Difficulty of Training Deep Feedforward Neural Networks by Xavier Glorot and Yoshua Bengio identified problems with the popular logistic sigmoid activation function and the weight initialization technique that was most popular at the time (namely random initialization using a normal distribution with a mean of 0 and a standard deviation of 1).
They showed that with this activation function and this initialization scheme, the variance of the outputs of each layer is much greater than the variance of its inputs. Going forward in the network, the variance keeps increasing after each layer until the activation function saturates at the top layers. This is actually made worse by the fact that the logistic function has a mean of 0.5, not 0 (the hyperbolic tangent function has a mean of 0 and behaves slightly better than the logistic function in deep networks).
Looking at the logistic activation function, when inputs become large (negative or positive), the function saturates at 0 or 1, with a derivative extremely close to 0. Thus when backpropagation kicks in, it has virtually no gradient to propagate back through the network, and what little gradient exists keeps getting diluted as backpropagation progresses down through the top layers, so there is really nothing left for the lower layers.
In their paper, Glorot and Bengio proposed a way to significantly alleviate this problem. The signal must flow properly in both directions: in the forward direction when making predictions, and in the reverse direction when backpropagating gradients. We don’t want the signal to die out, nor do we want it to explode and saturate. For the signal to flow properly, the authors argue that we need the variance of the outputs of each layer to be equal to the variance of its inputs, and we also need the gradients to have equal variance before and after flowing through a layer in the reverse direction.
One of the insights in the 2010 paper by Glorot and Bengio was that the vanishing/exploding gradients problems were in part due to a poor choice of activation function. Until then most people had assumed that if Nature had chosen to use roughly sigmoid activation functions in biological neurons, they must be an excellent choice. But it turns out that other activation functions behave much better in deep neural networks, in particular the ReLU activation function, mostly because it does not saturate for positive values (and also because it is quite fast to compute).
The Rectifier Linear Unit (ReLU) uses the following activation function $$ f(z) = \max(0,z). $$
The ReLU activation function suffers from a problem known as the dying ReLUs: during training, some neurons effectively die, meaning they stop outputting anything other than 0.
In some cases, you may find that half of your network’s neurons are dead, especially if you used a large learning rate. During training, if a neuron’s weights get updated such that the weighted sum of the neuron’s inputs is negative, it will start outputting 0. When this happen, the neuron is unlikely to come back to life since the gradient of the ReLU function is 0 when its input is negative.
To solve this problem, nowadays practitioners use a variant of the ReLU function, such as the leaky ReLU discussed above or the so-called exponential linear unit (ELU) function $$ ELU(z) = \left\{\begin{array}{cc} \alpha\left( \exp{(z)}-1\right) & z < 0,\\ z & z \ge 0.\end{array}\right. $$
In general it seems that the ELU activation function is better than the leaky ReLU function (and its variants), which is better than ReLU. ReLU performs better than \( \tanh \) which in turn performs better than the logistic function.
If runtime performance is an issue, then you may opt for the leaky ReLU function over the ELU function If you don’t want to tweak yet another hyperparameter, you may just use the default \( \alpha \) of \( 0.01 \) for the leaky ReLU, and \( 1 \) for ELU. If you have spare time and computing power, you can use cross-validation or bootstrap to evaluate other activation functions.
The first thing we would like to do is divide the data into two or three parts. A training set, a validation or dev (development) set, and a test set.
However, sometimes the training data and test data differ in subtle ways because, for example, they are collected using slightly different methods, or because it is cheaper to collect data in one way versus another. In this case, there can be a mismatch between the training and test data. This can lead to the neural network overfitting these small differences between the test and training sets, and a poor performance on the test set despite having a good performance on the validation set. To rectify this, Andrew Ng suggests making two validation or dev sets, one constructed from the training data and one constructed from the test data. The difference between the performance of the algorithm on these two validation sets quantifies the train-test mismatch. This can serve as another important diagnostic when using DNNs for supervised learning.
Like all statistical methods, supervised learning using neural networks has important limitations. This is especially important when one seeks to apply these methods, especially to physics problems. Like all tools, DNNs are not a universal solution. Often, the same or better performance on a task can be achieved by using a few hand-engineered features (or even a collection of random features).
Here we list some of the important limitations of supervised neural network based models.


{% extends "base/base.html" %} {% load bootstrap3 %} {% load static %} {% block head %} {% endblock %} {% block content %}
In other cases, the translation of the given fragment is incorrect. In that case, you should clear the checkbox {{ is_translation_title }}.


"WHEEL "(Hebrews galgal; rendered “wheel” in Psalm 83:13, and “a rolling thing” in Isaiah 17:13; R.V. in both, “whirling dust”). This word has been supposed to mean the wild artichoke, which assumes the form of a globe, and in autumn breaks away from its roots, and is rolled about by the wind in some places in great numbers.
"WHEEL "(Hebrews galgal; rendered “wheel” in Psalm 83:13, and “a rolling thing” in Isaiah 17:13; R.V. in both, “whirling dust”). This word has been supposed to mean the wild artichoke, which assumes the form of a globe, and in autumn breaks away from its roots, and is rolled about by the wind in some places in great numbers.


My Origin
Jim Henson's Designs and Doodles explains Cookie Monster's early life: "In 1966, Henson drew three monsters who appeared in a General Foods commercial that featured three crunchy snack foods: Wheels, Crowns and Flutes. Each snack was represented by a different monster. The Wheel-Stealer was a short, fuzzy monster with wonky eyes and sharply pointed teeth. The Flute-Snatcher was a speed demon with a long, sharp nose and windblown hair. The Crown-Grabber was a hulk of a monster with a Boris Karloff accent and teeth that resembled giant knitting needles.
"These monsters had insatiable appetites for the snack foods they were named after. Each time the Muppet narrator, a human-looking fellow, fixes himself a tray of Wheels, Flutes and Crowns, they disappear before he can eat them. One by one, the monsters sneak in and zoom away with the snacks. Frustrated and peckish, the narrator warns viewers that these pesky monsters could be disguised as someone in your own home, at which point the monsters briefly turn into people and then dissolve back to monsters again."
As it turns out, the commercial was never aired — but all three monsters had a future in the Muppet cast. The Crown-Grabber was used in an Ed Sullivan Show sketch, in which he ruins a girl's beautiful day. Known from then on as the Beautiful Day Monster, he made a number of appearances on Sesame Street and The Muppet Show. The Flute-Snatcher turned into Snake Frackle, a background monster from The Great Santa Claus Switch and The Muppet Show.
And then there's the Wheel-Stealer, who was destined for greater things.
In 1967, Henson used the Wheel-Stealer puppet for an IBM training film called "The Coffee Break Machine." In the sketch, the monster devoured a complex machine as the machine described its purpose and construction. His greed gets the better of him, however, as the machine's recording continues (within his stomach), announcing that it is wired to self-destruct. The monster promptly explodes. This sketch was also performed in October 1967 on The Ed Sullivan Show.
Two years later, a similar-looking puppet (sans teeth) was used for three commercials selling Munchos, a Frito-Lay potato chip. This time, the monster was called Arnold. After the three ads were produced, Henson had the opportunity to renew the contract. He chose not to, because at that point he was working on Sesame Street -- and that monster puppet was moving on to the next stage in his career.


Thank you for signing up with the QA4SM Validation Service. You will be notified via email as soon as your account is activated by an admin.


---
layout: post
title: 5/26 school
date: '2014-05-26T21:50:00.001-06:00'
author: Jacob .
tags:
- homeschool-journal
modified_time: '2014-05-26T21:50:23.920-06:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-3142481826866019159
blogger_orig_url: http://www.svlark.com/2014/05/526-school.html
---
Math
- 40 subtraction facts for 10 in splash math
History
- read the first section of SotWV1, Chapter 34 on Julius Caesar
- answered review questions
- narration and dictation
- map work to finish SotWV1, Chapter 29 on the Punic Wars
Composition
- WWE: 1 Week 31: Days 1, 2
Reading
- Audrey read aloud two chapters from Emmaline and the Bunny
- April read to the girls a chapter of The Happy Hollisters


Unfortunately Instagram doesn't even have an official widget, so I can't include my IG timeline, they need to step up their game.
For direct contact you can email me<EMAIL_ADDRESS>or to purchase/order something you can email<EMAIL_ADDRESS>I am a free-lance web devoloper and graphic designer, and I am able to design your site. Before deciding to order a site, you must remember that I am still relatively new to web development. In terms of graphic design, I am experienced and able to do what you ask, as long as it isn't an illustration.
Twitter: @GriffIsKingIncludes all projects I've done for school, and even at home like this one. All of my projects are open source, and you get to view all resources I used.
This is all of the websites or graphics I've done. Different from projects, you don't get to view all of my sources or have detailed descriptions. This is to quickly view what I've done.


Stefano Pioli: Zlatan Ibrahimovic is a great champion, but AC Milan not focused on January
by FSA EditorZlatan Ibrahimovic is a great champion, says Stefano Pioli, but AC Milan’s coach is not focusing on new signings just yet.
AC Milan coach Stefano Pioli has repeated his praise of Zlatan Ibrahimovic, but insists the Serie A club are not yet focusing on new signings.
Ibrahimovic has been linked with a return to Milan, where he spent two seasons between 2010 and 2012 before moving to Paris Saint-Germain, after the 38-year-old left MLS club LA Galaxy.
Pioli has previously lauded Ibrahimovic, however, he insisted it was too early to talk about the upcoming transfer window in January.
Milan’s coach was pressed for his thoughts on Ibrahimovic – who is now reported to be a target for new Tottenham boss Jose Mourinho – ahead of Saturday’s clash with Napoli, but Pioli reiterated the Rossoneri are not yet focused on January arrivals.
“I have already explained what I think, he is a great champion for his technical skills and his professionalism,” Pioli told a news conference.
“I end it here, our present is too important.
“With the team I have to think only of the field and tomorrow’s match. For judgments on the market, I will do it later, we are only in November.
“We are focused on the difficult and very important match of tomorrow. We have to give 200 per cent and that’s what I expect from the team.”
Milan sit 14th in Serie A, having taken just 13 points from their opening 12 matches of the season.
However, Pioli believes Milan’s display in a 1-0 defeat to Juventus before the international break gives his side a platform from which to build on.
“It starts from the performance in Turin, which was against one of the best in Europe,” Pioli added.
“We now play against a strong team like Napoli which is equipped for the Champions League and to win the Scudetto. Napoli are strong but we are not far behind.”


|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object morfologik.stemming.Dictionary
public final class Dictionary
A dictionary combines FSA
automaton and metadata describing the
internals of dictionary entries' coding (DictionaryMetadata
.
A dictionary consists of two files:
| Field Summary | |
|---|---|
static java.util.WeakHashMap<java.lang.String,Dictionary> |
defaultDictionaries
Default loaded dictionaries. |
FSA |
fsa
FSA automaton with the compiled dictionary data. |
DictionaryMetadata |
metadata
Metadata associated with the dictionary. |
static java.lang.String |
METADATA_FILE_EXTENSION
Expected metadata file extension. |
| Constructor Summary | |
|---|---|
Dictionary(FSA fsa,
DictionaryMetadata metadata)
It is strongly recommended to use static methods in this class for reading dictionaries. |
| Method Summary | |
|---|---|
static java.lang.String |
getExpectedFeaturesName(java.lang.String name)
Returns the expected name of the metadata file, based on the name of the FSA dictionary file. |
static Dictionary |
getForLanguage(java.lang.String languageCode)
Return a built-in dictionary for a given ISO language code. |
static Dictionary |
read(java.io.File fsaFile)
Attempts to load a dictionary using the path to the FSA file and the expected metadata extension. |
static Dictionary |
read(java.net.URL fsaURL)
Attempts to load a dictionary using the URL to the FSA file and the expected metadata extension. |
static Dictionary |
readAndClose(java.io.InputStream fsaData,
java.io.InputStream featuresData)
Attempts to load a dictionary from opened streams of FSA dictionary data and associated metadata. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final java.lang.String METADATA_FILE_EXTENSION
public final FSA fsa
FSA
automaton with the compiled dictionary data.
public final DictionaryMetadata metadata
public static final java.util.WeakHashMap<java.lang.String,Dictionary> defaultDictionaries
| Constructor Detail |
|---|
public Dictionary(FSA fsa, DictionaryMetadata metadata)
fsa
- An instantiated FSA
instance.metadata
- A map of attributes describing the compression format and
other settings not contained in the FSA automaton. For an
explanation of available attributes and their possible values,
see DictionaryMetadata
.| Method Detail |
|---|
public static Dictionary read(java.io.File fsaFile) throws java.io.IOException
java.io.IOException
public static Dictionary read(java.net.URL fsaURL) throws java.io.IOException
Attempts to load a dictionary using the URL to the FSA file and the expected metadata extension.
This method can be used to load resource-based dictionaries, but be aware of JAR resource-locking issues that arise from resource URLs.
java.io.IOException
public static Dictionary readAndClose(java.io.InputStream fsaData, java.io.InputStream featuresData) throws java.io.IOException
java.io.IOException
public static java.lang.String getExpectedFeaturesName(java.lang.String name)
name
and appending
METADATA_FILE_EXTENSION
.
public static Dictionary getForLanguage(java.lang.String languageCode)
java.lang.RuntimeException
- Throws a RuntimeException
if the dictionary is not
bundled with the library.
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Summary statistics for the stimuli used in the experiment
a F-K grade = Flesch–Kincaid readability index (Kincaid, Fishburne, Rogers, & Chissom, 1975)
b Original Flesch readability index (Flesch, 1948)


Generating code with NTL
Generating code using System.CodeDOM
NMF Transformations does not have any restrictions regarding the input or output models regarding conformance to a framework (such as Models). Thus, it is a neat approach to reuse code models that are widely accepted, such as the code model of the System.CodeDOM namespace. The advantage of this code model is that there are plenty of code generators available that transform these models in a variety of languages. These languages include:
- C#
- VB.NET
- C++/CLI
- F#
- JavaScript.NET
Thus, it is not by chance that these languages are exactly the languages supported by Ecore2Code, the code generator for Ecore metamodels provided by EcoreInterop.
To specify such a code generator, one simply has to write a model transformation that transforms a model into a System.CodeDOM model and then transform this model into code by using a suitable predefined code generator.
(An example is soon to follow)
Roslyn
However, System.CodeDOM has some strong limitations, as it supports neither lambda expressions nor operator overloading nor a biwise Xor operator. This is due to the fact that System.CodeDom was initially created rather to represent the common concepts of multiple languages, only.
One way out of this dilemma is Roslyn, the brand new open compiler API from Microsoft. The code model used by Roslyn is specific to the used language, so all features of the language can be used. The model is also more fine-grained, so the code generation can be more precise.
However, the code model is also immutable, mainly for reasons of clean parallel programming. This makes the Roslyn model absolutely unsuitable for code generation. So, to use Roslyn and NTL, the very best idea is to wait for some mutable Roslyn wrapper.


Ele (pronounced "Ellie") is a browser-based editing environment centered on creating reusable custom elements compatible with the Web Components specifications.
## Component Editor
The heart of Ele is the Component Editor, a simple "fiddle-esque" environment for building custom elements. The component editor has three main pieces:
1. **Element Source Panel:** This is where you will write the actual source code for your custom element. For now, Ele is designed around creating elements based on the [Polymer](http://www.polymer-project.org/) library. We hope to expand this in the near future to support X-Tags as well as vanilla custom elements.
2. **Demo Source Panel:** The demo source panel is where you will showcase the usage of your custom element. A good demo will help people immediately understand what your element can do as well as some of the basic options for it.
3. **Preview Panel:** This is a live-updating preview of your element's demo. As you change the source in either of the other two panels, this will automatically update.
Your code is continuously saved while you work.
### Element Package Name
Ele will automatically name your package based on the first `polymer-element` you define in it. When you create a new element it will be assigned a random name, and the name will be updated whenever you change it in the source.
### Using Imported Elements
Ele is designed to mirror as closely as possible environment of a reusable component distributed via Bower. To that end, we have made available Polymer, the `core-elements` and the `paper-elements` by using relative URLs. For example, if you want to use `core-ajax` in your element, you simply need to add the relevant `` tag:
For now only the core and paper elements are available for import; however, we have hopes of making fully customizable bower dependencies a reality for Ele in the near future.
**Note:** You should make sure to leave the `polymer.html` import in your source, as you will need it to be able to define a `polymer-element`.
### Documenting Your Element
Ele allows you to write documentation for your element directly into the source code. Utilizing Polymer's [core-doc-viewer](http://www.polymer-project.org/components/core-doc-viewer/demo.html), Ele will automatically parse Markdown comments in your element to turn them into documentation, which is visible on your element's public page.
## Sharing Elements
At any time you can click the **Share Button** in the editor toolbar to open the public page for your element. The public page shows a read-only version of your source code along-side your demo and any documentation parsed out from the comments in your source code. You can share a link to your element's public page. The public page reflects the most recent save of an element.
For now, sharing an element simply serves as a good way to quickly view the source code and a live demo of a custom element. In the future we plan to make it possible to tag versioned releases of an element and even publish it to Bower!
## Ele Roadmap
Ele is an [open source](https://github.com/divshot/ele-web) community effort by [Divshot](http://www.divshot.com/). We hope to create a fast, easy-to-use environment for quickly crafting reusable Web Components. To that end, here are some of the features we have planned:
* **Bower Dependencies:** We aim to make it possible to specify Bower dependencies for your element so that you can have a truly local-esque environment for working on your elements.
* **Publish to Gist:** You will be able to publish your element to a Gist (including publishing new versions of that element).
* **Bower Installable:** Custom elements hosted on Ele will be able to be installed (with semantic versioning) via Bower, making it not just a playground but a feasible element distribution platform!
* **Browseable Directory:** Any element with a published release will be put into a browseable element catalog.
* **Beyond Polymer:** We want to make sure that Ele is compatible with any custom element libraries, not just Polymer.
Is there something you'd love to see that we haven't thought of yet? Let us know on [GitHub Issues](https://github.com/divshot/ele-web/issues).


|
|
|
|
|
| Select | |
Open Position | {{row.positionName}} | Position ID | {{row.positionID}} | Account | {{row.account}} | Job Role | {{row.openPosition_jrss[0].jrss}} | Line Of Business | {{row.lineOfBusiness}} | Position Location | {{row.positionLocation}} | Rate Card Job Role | {{row.rateCardJobRole}} | Competency Level | {{row.competencyLevel}} |
|---|
| No Records Found! |
| Select | |
Open Position | {{row.positionName}} | Position ID | {{row.positionID}} | Account | {{row.account}} | Job Role | {{row.openPosition_jrss[0].jrss}} | Line Of Business | {{row.lineOfBusiness}} | Position Location | {{row.positionLocation}} | Rate Card Job Role | {{row.rateCardJobRole}} | Competency Level | {{row.competencyLevel}} |
|---|
| No Records Found! |


Before Change
Reference: Weinreb et al. (2017).
if issparse(X):
raise ValueError("Not defined for sparse input. See `filter_genes_dispersion`.")
mean_filter = np.mean(X, axis=0) > Ecutoff
var_filter = np.std(X, axis=0) / (np.mean(X, axis=0) + .0001) > cvFilter
gene_subset = np.nonzero(np.all([mean_filter, var_filter], axis=0))[0]
return gene_subset
def filter_genes_fano_deprecated(X, Ecutoff, Vcutoff):
After Change
def filter_genes_cv_deprecated(X, Ecutoff, cvFilter):
Filter genes by coefficient of variance and mean.
return _filter_genes(X, Ecutoff, cvFilter, np.std)
def filter_genes_fano_deprecated(X, Ecutoff, Vcutoff):
Filter genes by fano factor and mean.


The test passes if:
- The two squares each contain five green horizontal stripes
- There are two green horizontal stripes below the square on right
- There is no red
XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX


Uh-oh!
There’s a third particle here!
Will there be a fight?
No, they all like each other equally!
Move them around a bit and you’ll notice that they don’t just move towards the pointer. They also rotate around each other!
Uh-oh!
There’s a third particle here!
Will there be a fight?
No, they all like each other equally!
Move them around a bit and you’ll notice that they don’t just move towards the pointer. They also rotate around each other!


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.api.mvc.Request @import play.api.i18n.Messages @import config.AppConfig @import views.html.templates.agent_main_template @import uk.gov.hmrc.play.views.html.helpers.form @import views.html.helpers.continueButton @import views.html.helpers.signOutLink @import views.html.helpers.back_link @()(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @agent_main_template(title = messages("agent.cannot_take_part.title")) { @back_link("javascript:history.back()")
@messages("agent.cannot_take_part.income")
@messages("agent.cannot_take_part.other")
@Html(messages( "agent.cannot_take_part.alternative", {messages("agent.cannot_take_part.alternative.link")} ))
@form(action = controllers.agent.eligibility.routes.Covid19ClaimCheckController.show()) {


Using Menu Commands
Below here is the explanation of the various commands.
Actions:
- New - creates a new grid in a new tab
- Import - loads a grid from a file
- Clear - erases the current grid
- Clear Sparks - erases the current grid from all the sparks
- Clear Links - erases the current grid from all the links
- Export - saves the current grid in a file
- Save - saves the current grid status
- Quit - to exit from the application
Edit:
- Undo - undoes the last action
- Redo - redoes the previous action
- Cut - cuts the entities from the current selection
- Copy - copies the entities from the current selection
- Paste - pastes some entities from the clipboard to the grid
- Flip horizontally - saves the current grid in a file
- Flip vertically - saves the current grid status
- Rotate 90 deg - rotates by 90 degrees the current selection
- Rotate 180 deg - rotates by 180 degrees the current selection
- Rotate 270 deg - rotates by 270 degrees the current selection
Environment:
- Smart selection - enables/disables smart selection
- Minimap - enables/disables minimap
- Chess creation - enables/disables chess method for the creation of entities
- Play - starts the simulation on the current grid
- Step - executes only one step of the simulation for che current grid
- Stop - stops the simulation on the current grid
Items:
- Spark - inserts entities of Spark type
- Arrow UP - inserts entities of Arrow UP type
- Arrow DOWN - inserts entities of Arrow DOWN type
- Arrow RIGHT - inserts entities of Arrow RIGHT type
- Arrow LEFT - inserts entities of Arrow LEFT type
- 1D Automata - inserts entities of 1D Automata type (with rule 30 as default)
- Link IN - creates a link of type IN
- Link OUT - creates a link of type OUT
- Links Manager - opens the links manager dialog
Info:
- Cellular Automata Manager - opens a dialog with info about this program
- Help - opens the current guide


Time Out: GAA on this day, quick quiz & sporting birthdays
by Padraig O MeiscillDINNY Cahill's 200-mile treks to Belfast are set to resume after his decision to accept the Antrim senior hurling manager's position for a second time.
The Tipperary native previously managed the Saffrons from 2002 to 2005 and fills the post that was vacated by Terence McNaughton and Dominic McKinley in the summer.
“I believe that I now have a chance to address what feels like unfinished business,” said Cahill after accepting the role.
“When I first went up to Antrim, I had just finished coaching Tipperary minors and we were lucky enough to achieve All-Ireland success at that time. I was relatively inexperienced then as far as senior inter-county management was concerned.
“Through my previous involvement with Antrim and, more recently, with Portumna and Graigue-Ballycallan, I have gained extensive experience to assist me in this new challenge.
“With the right commitment from players, clubs and supporters in Antrim, we can build a more consistent and successful future for the county.''
ON THIS DAY IN SPORT
1989: David O'Leary's 622nd game for Arsenal, a club record, was marred by a mass brawl with opponents Norwich at Highbury. Arsenal were awarded a stoppage-time penalty and won 4-3.
1991: England midfielder Bryan Robson announced his international retirement after being left out of the squad for a game against Poland. He won 90 caps for his country.
2002: England's Luke Donald claimed his first win on the USPGA Tour when rain washed out the final round of the Southern Farm Bureau Classic in Mississippi, reducing the tournament to 54 holes.
2007: Joe Calzaghe won his world super-middleweight title unification fight against Denmark's Mikkel Kessler with a unanimous decision victory.
2008: The Argentinian Football Association appointed Diego Maradona as the new head coach of the national team.
2015: British racing driver Susie Wolff announced that she would retire from motor sport at the end of the season, having given up hope of competing in Formula One.
2016: FIFA launched disciplinary proceedings against the Football Association of Ireland after the Republic of Ireland national team wore shirts bearing a poppy symbol, representing the 100th anniversary of the Easter Rising, for a friendly against Switzerland.
2017: Celtic set a new record for the longest unbeaten run in domestic competition by a British top-flight team, extending their sequence to 63 games by beating St Johnstone 4-0.
SPORT BIRTHDAYS
Rod Marsh (cricket) - former Australia wicketkeeper, born 1947.
Nigel Worthington (soccer) - former Northern Ireland defender and manager, born 1961.
Gary Havelock (speedway) - 1992 world champion, born 1968.
Luis Figo (soccer) - former Inter Milan, Barcelona, Real Madrid and Portugal winger, born 1972.
Mario Melchiot (soccer) - former Wigan, Birmingham, Chelsea and Holland defender, born 1976.
Devin Hester (American football) - former Seattle Seahawks wide receiver who holds the NFL record for the most returning touchdowns, born 1982.
Chris Martin (soccer) - Derby striker and Scotland international, born 1988.
QUICK QUIZ
1. How many times have Cuala (Dublin) won the Leinster Club SHC?
2. Which country recently beat the Australian rugby league team for the first time?
3. With which club has Mickey Linden won eight Down SFC titles?
4. Who are the reigning Super Bowl champions?
5. How many Grand Nationals did AP McCoy win?
(See answers below)
SPORT ON TV
Monday, November 4
CRICKET: T20, New Zealand v England - Sky Sports Main Event, Sky Sports Cricket 0030 (Tue).
SOCCER: Championship, Stoke v West Brom - Sky Sports Main Event, Sky Sports Football 1930.
SNOOKER: Champion of Champions - ITV 4 1245 & 1845.
GRIDIRON: NFL, New York Giants v Dallas Cowboys - Sky Sports Action 0100 (Tue).
QUIZ ANSWERS
1. Twice (2017 & '18); 2. Tonga; 3. Mayobridge; 4. New England Patriots; 5. One